Improved estimation of supervision in unsupervised speaker adaptation

Unsupervised speaker adaptation plays an important role in "batch dictation", the aim of which is to automatically transcribe large amounts of recorded dictation using speech recognition. In the case of unsupervised speaker adaptation which uses recognition results of target speech as the means of supervision, erroneous recognition results degrade the quality of the adapted acoustic models. This paper presents a new supervision selection method. By using this method, correction of the first candidate is judged based on the likelihood ratio of the first and the second candidates. This method eliminates erroneous recognition results and corresponding speech data from the adaptive training data. We implemented this method in the iterative unsupervised speaker adaptation procedure. It is shown that the recognition errors are drastically reduced by 50% in a practical application of batch-style speech-to-text conversion of recorded dictation of Japanese medical diagnoses compared with speaker-independent recognition.